A good forecast brings together a myriad of factors which are often considered separately - evolution of the target market, role of the product, segments of customers, competition, impact of investments which will help the product succeed, etc. The forecast provides a single place for the company to consider this range of topics and how they come together.

In a way, a good forecast is like a 360-view, where each input represents an angle of the whole picture. The forecast brings these inputs together and provides a full image.

New advances enable companies to make this “full image” sharper and more representative of reality

Today’s increasing amounts of data and new technologies to use that data enable forecasts to provide more insights faster. These advances, combined with good methodology, can make the panorama come to life. By leveraging automation, machine learning analytics, and access to vast global healthcare data sets, forecasters can now deliver insightful, evidence-based forecasts in almost real-time, with answers to questions stakeholders are asking.

It’s a powerful value proposition, but what can forecasters and teams do to move in this direction? We suggest five key practices that can help forecasters put a vibrant 360-view together.

Use the best market data available. With the massive amounts of healthcare data available today, it’s possible to go far beyond the scope of a simple epi-report. There are many different types of real-world data which can give forecasters a clearer view of what’s actually going on in the market, such as:

Numbers of incident, switching, and continuing patients

Sales volumes by channel, prescriber type, and other breakdowns

Global sales and volume data showing commercial trends by country

…and much more!

Not only is more data available, but today’s forecasting platforms make it possible to process this real-world data to answer questions about the marketplace and understand how best to access it, making it a vital part of commercial decisions.

Validate forecasts with benchmarks. The best forecasts are closely connected to real-world scenarios. Forecasters should validate and challenge assumptions with comparisons to those real-world situations.

One of the ways to do that is with analogues – actual products with similar market dynamics to the forecasted product. This comparison against actual products on the market helps teams validate or challenge their assumptions more effectively. For example, it’s easy for forecasters to assume that physicians will rapidly adapt their product, but the reality for many products is that there are significant challenges with gaining market share after launch. Analogues show the true adoption curve for other products, and in turn, may enable forecasters to make better assumptions.

This brings us to another point - forecasting should not be used as a way to document or back into gut assumptions. Use of analogues and other benchmarks to calibrate forecast inputs encourages an unbiased, evidence-based view of product potential.

Don’t get stuck in the base case.Life sciences markets are becoming increasingly dynamic, with items such as more frequent launches, changing standards of care, and new access environments. As a result, the base case is often not enough to give a complete picture of a product’s future.

Forecasters should consider a range of scenarios to reflect potential future market conditions, as these considerations will affect decision-making today. To be comprehensive, the set of scenarios in the forecast should cover cases where your company makes different decisions, as well as cases where the market turns out differently.

Forecasters should leverage analytics and use systems that make these analytics user-friendly. For example, Monte Carlo analysis runs thousands of simulations and outputs the range and probability of different overall outcomes for revenue. Additionally, Sensitivity analysis shows how much an outcome would change based on variation of a single input, so companies can identify the most relevant forecast drivers.

When the world is uncertain, these analyses help your company make more informed decisions in planning for the future.

Apply the right level of granularity for your business problems.Forecasters can move faster to insights when they think through the specific business situations they want to address and structure their analysis accordingly. For example:

If the challenge in the market is how to serve a certain segment, then the forecast should model down to that segment level

If the issue is that competitors are changing lines of therapy they treat, the model needs to capture the patient flow across those therapy lines

If the issue is understanding the maximum level of discount allowed to still be profitable, the model needs to capture different payer channels

If the issue is quantifying disruption of an existing product line with a new product, the model should have a cannibalization engine

As with any decision, setting the model granularity requires evaluating tradeoffs between cost and benefit. Forecasters should work with management to identify these key questions and the associated key metrics which will drive business decisions. That way, efforts are focused where they matter most, and more time is spent answering questions which actually move the needle.

Share results in real-time. Making the forecast easier to access allows it to provide better value to the organization. One way to do this is to move towards sharing forecast results with live web dashboards. Depending on company culture, forecasters may even want to enable access to these dashboards on-demand for key decision-makers. Forecasters can now enable stakeholders to visualize scenarios and see insights live, even real-time in meetings where decisions are being made.

And when a forecast helps your organization make better, more evidence-driven decisions, share the positive outcomes and best practices with all company stakeholders to keep the momentum going.